In a computing network environment, multiple computing resources are deployed at different geographical locations in order to execute jobs associated with software applications of different technologies. The execution of the jobs is managed through a service level agreement (SLA) between a service provider and a customer. The service provider facilitates the execution of the jobs belonging to the customer. The customer may prescribe its requirements regarding the execution of the jobs through the SLA. In one example, the customer may set a pre-defined time limit for the execution of the jobs. Based on the requirements of the customer, the service provider may have to accordingly plan and execute the jobs, so that the SLA is complied.
In order to comply with the SLA, the service provider may have to effectively analyze supply and demand of the computing resources, such that the jobs are executed within the pre-defined time limit set as per the SLA. However, since the arrival of the jobs is dynamic, it becomes a technical challenge to effectively plan and allocate the computing resources. In view of lack of the effective planning and the allocation of the computing resources, it may be observed that few of the computing resources are overloaded with more jobs while the other computing resources may be receiving fewer jobs or may be idle. This may lead to ineffective utilization of the computing resources and hence may affect the completion of the execution of the jobs as per the SLA.